一种基于历史背景的混合高斯背景建模算法

针对背景场景重复显现问题,提出了一种基于历史背景的混合高斯模型(History Background-based GMM,HBGMM).相较于传统的混合高斯模型,该模型对历史背景模型进行标记,并通过判决匹配次数快速调整历史背景模型的学习率.同时对模型权重低于阈值下限历史模型和非历史模型进行区别处理,用该方法更新模型权重从而降低误检率,使历史模型尽量避免误删除.实验结果表明,本文提出的基于历史背景的混合高斯背景模型能够实现记忆背景的功能,从而更快地适应场景的变化,减少前景误判.

A History Background-based Gaussian Mixture Background Modeling Algorithm

Classical Gaussian mixture model (GMM) can describe the multimodal state of the video pixels and GMM has certain robustness in dealing with complex scenes, such as slowly changing lighting. However, it still causes false detection because of the change of pixel values in the same position when the background of the scene is re-exposed after being covered. To solve the repetitive background problem, a Gaussian mixture model based on history background (HBGMM) was proposed in this paper. Compared with traditional Gaussian mixture model, this model can quickly adjust the learning rate by marking the historical background and counting the matched times. We also processed differently between the historical and non-historical model weights lower than threshold to update the model weights to reduce the false detection rate. Experiment results show that the proposed HBGMM can realize the function of remembering the scenes and adapt to the changes of scenes more quickly, thus decreasing the false detection rate.